Overview

Dataset statistics

Number of variables18
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 MiB
Average record size in memory144.0 B

Variable types

Numeric16
Categorical2

Alerts

rerun_ID has constant value ""Constant
u is highly skewed (γ1 = -313.8492424)Skewed
g is highly skewed (γ1 = -314.2766946)Skewed
z is highly skewed (γ1 = -314.7594188)Skewed
spec_obj_ID has unique valuesUnique

Reproduction

Analysis started2024-03-11 00:33:07.319695
Analysis finished2024-03-11 00:33:30.504136
Duration23.18 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

obj_ID
Real number (ℝ)

Distinct78053
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2376647 × 1018
Minimum1.2376459 × 1018
Maximum1.2376805 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:30.573861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.2376459 × 1018
5-th percentile1.2376515 × 1018
Q11.2376592 × 1018
median1.2376635 × 1018
Q31.2376684 × 1018
95-th percentile1.23768 × 1018
Maximum1.2376805 × 1018
Range3.4588452 × 1013
Interquartile range (IQR)9.1890913 × 1012

Descriptive statistics

Standard deviation8.4385599 × 1012
Coefficient of variation (CV)6.8181307 × 10-6
Kurtosis-0.60616818
Mean1.2376647 × 1018
Median Absolute Deviation (MAD)4.8683572 × 1012
Skewness0.39872718
Sum1.2376647 × 1023
Variance7.1209293 × 1025
MonotonicityNot monotonic
2024-03-11T01:33:30.688316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237659326 × 101817
 
< 0.1%
1.237663463 × 101813
 
< 0.1%
1.237666302 × 101812
 
< 0.1%
1.237663785 × 101811
 
< 0.1%
1.237663463 × 101811
 
< 0.1%
1.237666302 × 101811
 
< 0.1%
1.237666302 × 101811
 
< 0.1%
1.23766634 × 101811
 
< 0.1%
1.237663463 × 101810
 
< 0.1%
1.237658206 × 101810
 
< 0.1%
Other values (78043) 99883
99.9%
ValueCountFrequency (%)
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10184
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10182
< 0.1%
1.237645943 × 10181
 
< 0.1%
1.237645943 × 10182
< 0.1%
1.237645943 × 10181
 
< 0.1%
ValueCountFrequency (%)
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10182
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%
1.237680531 × 10181
< 0.1%

alpha
Real number (ℝ)

Distinct99999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.62912
Minimum0.0055278279
Maximum359.99981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:30.799085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0055278279
5-th percentile11.750808
Q1127.51822
median180.9007
Q3233.895
95-th percentile348.76982
Maximum359.99981
Range359.99428
Interquartile range (IQR)106.37678

Descriptive statistics

Standard deviation96.502241
Coefficient of variation (CV)0.54327941
Kurtosis-0.5371908
Mean177.62912
Median Absolute Deviation (MAD)53.2025
Skewness-0.028510865
Sum17762912
Variance9312.6825
MonotonicityNot monotonic
2024-03-11T01:33:30.902791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.74960002 2
 
< 0.1%
135.6891066 1
 
< 0.1%
122.7363106 1
 
< 0.1%
28.44961333 1
 
< 0.1%
216.3553482 1
 
< 0.1%
337.1751199 1
 
< 0.1%
304.8549412 1
 
< 0.1%
119.2801296 1
 
< 0.1%
258.089669 1
 
< 0.1%
256.7709465 1
 
< 0.1%
Other values (99989) 99989
> 99.9%
ValueCountFrequency (%)
0.005527827924 1
< 0.1%
0.01095869374 1
< 0.1%
0.01168374605 1
< 0.1%
0.01333666183 1
< 0.1%
0.0229663342 1
< 0.1%
0.02425788497 1
< 0.1%
0.02461910532 1
< 0.1%
0.02571591573 1
< 0.1%
0.02720599703 1
< 0.1%
0.02893798287 1
< 0.1%
ValueCountFrequency (%)
359.9998098 1
< 0.1%
359.9996152 1
< 0.1%
359.9990313 1
< 0.1%
359.9989651 1
< 0.1%
359.9970063 1
< 0.1%
359.9941245 1
< 0.1%
359.9936716 1
< 0.1%
359.9935681 1
< 0.1%
359.9893865 1
< 0.1%
359.9863626 1
< 0.1%

delta
Real number (ℝ)

Distinct99999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.135305
Minimum-18.785328
Maximum83.000519
Zeros0
Zeros (%)0.0%
Negative12061
Negative (%)12.1%
Memory size781.4 KiB
2024-03-11T01:33:31.008975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-18.785328
5-th percentile-2.3656696
Q15.1467709
median23.645922
Q339.90155
95-th percentile56.701075
Maximum83.000519
Range101.78585
Interquartile range (IQR)34.754779

Descriptive statistics

Standard deviation19.644665
Coefficient of variation (CV)0.81393899
Kurtosis-1.0430622
Mean24.135305
Median Absolute Deviation (MAD)17.268085
Skewness0.17507856
Sum2413530.5
Variance385.91288
MonotonicityNot monotonic
2024-03-11T01:33:31.111876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6019312345 2
 
< 0.1%
32.49463184 1
 
< 0.1%
37.97318516 1
 
< 0.1%
6.153946054 1
 
< 0.1%
45.3358889 1
 
< 0.1%
4.94093746 1
 
< 0.1%
-11.90668825 1
 
< 0.1%
22.38564551 1
 
< 0.1%
24.85135128 1
 
< 0.1%
26.47819984 1
 
< 0.1%
Other values (99989) 99989
> 99.9%
ValueCountFrequency (%)
-18.78532808 1
< 0.1%
-17.63619832 1
< 0.1%
-17.61305644 1
< 0.1%
-17.57382002 1
< 0.1%
-17.46539063 1
< 0.1%
-17.45139007 1
< 0.1%
-17.4339462 1
< 0.1%
-17.36304673 1
< 0.1%
-17.28479137 1
< 0.1%
-17.22626675 1
< 0.1%
ValueCountFrequency (%)
83.00051859 1
< 0.1%
82.94762183 1
< 0.1%
82.81603025 1
< 0.1%
82.76442096 1
< 0.1%
82.75715318 1
< 0.1%
82.65631855 1
< 0.1%
82.56750029 1
< 0.1%
82.28865698 1
< 0.1%
79.34307616 1
< 0.1%
79.28929904 1
< 0.1%

u
Real number (ℝ)

SKEWED 

Distinct93748
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.980468
Minimum-9999
Maximum32.78139
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size781.4 KiB
2024-03-11T01:33:31.214544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile18.43093
Q120.352353
median22.179135
Q323.68744
95-th percentile25.78505
Maximum32.78139
Range10031.781
Interquartile range (IQR)3.3350875

Descriptive statistics

Standard deviation31.769291
Coefficient of variation (CV)1.4453419
Kurtosis98998.365
Mean21.980468
Median Absolute Deviation (MAD)1.666855
Skewness-313.84924
Sum2198046.8
Variance1009.2878
MonotonicityNot monotonic
2024-03-11T01:33:31.320135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.63465 77
 
0.1%
24.63466 64
 
0.1%
24.63467 44
 
< 0.1%
24.63464 23
 
< 0.1%
24.63468 20
 
< 0.1%
24.63463 5
 
< 0.1%
24.63469 4
 
< 0.1%
24.26353 4
 
< 0.1%
24.63462 4
 
< 0.1%
20.59248 4
 
< 0.1%
Other values (93738) 99751
99.8%
ValueCountFrequency (%)
-9999 1
< 0.1%
10.99623 1
< 0.1%
12.10168 1
< 0.1%
12.2624 1
< 0.1%
12.30349 1
< 0.1%
12.99664 1
< 0.1%
13.89799 1
< 0.1%
13.94716 1
< 0.1%
14.14713 1
< 0.1%
14.15199 1
< 0.1%
ValueCountFrequency (%)
32.78139 1
< 0.1%
30.66039 1
< 0.1%
29.32565 1
< 0.1%
29.23438 1
< 0.1%
29.19901 1
< 0.1%
29.18637 1
< 0.1%
29.04068 1
< 0.1%
28.90174 1
< 0.1%
28.79676 1
< 0.1%
28.77812 1
< 0.1%

g
Real number (ℝ)

SKEWED 

Distinct92651
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.531387
Minimum-9999
Maximum31.60224
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size781.4 KiB
2024-03-11T01:33:31.423758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile17.07282
Q118.96523
median21.099835
Q322.123767
95-th percentile23.408262
Maximum31.60224
Range10030.602
Interquartile range (IQR)3.1585375

Descriptive statistics

Standard deviation31.750292
Coefficient of variation (CV)1.5464271
Kurtosis99178.147
Mean20.531387
Median Absolute Deviation (MAD)1.3325
Skewness-314.27669
Sum2053138.7
Variance1008.0811
MonotonicityNot monotonic
2024-03-11T01:33:31.529052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.11438 9
 
< 0.1%
25.11439 9
 
< 0.1%
25.11437 8
 
< 0.1%
20.98246 4
 
< 0.1%
22.56372 4
 
< 0.1%
22.03644 4
 
< 0.1%
20.4269 4
 
< 0.1%
21.94795 4
 
< 0.1%
21.65138 4
 
< 0.1%
21.40104 4
 
< 0.1%
Other values (92641) 99946
99.9%
ValueCountFrequency (%)
-9999 1
< 0.1%
10.4982 1
< 0.1%
10.51139 1
< 0.1%
10.6718 1
< 0.1%
10.73097 1
< 0.1%
11.33897 1
< 0.1%
11.39234 1
< 0.1%
11.47435 1
< 0.1%
11.74518 1
< 0.1%
11.79892 1
< 0.1%
ValueCountFrequency (%)
31.60224 1
< 0.1%
30.607 1
< 0.1%
29.86258 1
< 0.1%
28.9032 1
< 0.1%
28.2066 1
< 0.1%
27.89482 1
< 0.1%
27.53211 1
< 0.1%
27.41465 1
< 0.1%
27.38851 1
< 0.1%
27.34772 1
< 0.1%

r
Real number (ℝ)

Distinct91901
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.645762
Minimum9.82207
Maximum29.57186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:31.632504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.82207
5-th percentile16.393046
Q118.135828
median20.12529
Q321.044785
95-th percentile22.070249
Maximum29.57186
Range19.74979
Interquartile range (IQR)2.9089575

Descriptive statistics

Standard deviation1.8547597
Coefficient of variation (CV)0.094410167
Kurtosis-0.37615756
Mean19.645762
Median Absolute Deviation (MAD)1.24741
Skewness-0.50785843
Sum1964576.2
Variance3.4401335
MonotonicityNot monotonic
2024-03-11T01:33:31.743548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.80203 21
 
< 0.1%
24.80202 15
 
< 0.1%
24.80204 7
 
< 0.1%
20.72245 6
 
< 0.1%
20.74657 5
 
< 0.1%
20.76527 5
 
< 0.1%
20.64847 4
 
< 0.1%
21.49762 4
 
< 0.1%
20.28744 4
 
< 0.1%
19.61753 4
 
< 0.1%
Other values (91891) 99925
99.9%
ValueCountFrequency (%)
9.82207 1
< 0.1%
10.06854 1
< 0.1%
10.11604 1
< 0.1%
10.1946 1
< 0.1%
10.80343 1
< 0.1%
10.86379 1
< 0.1%
10.98255 1
< 0.1%
11.09069 1
< 0.1%
11.35229 1
< 0.1%
11.64166 1
< 0.1%
ValueCountFrequency (%)
29.57186 1
< 0.1%
29.37411 1
< 0.1%
27.62688 1
< 0.1%
27.59332 1
< 0.1%
27.39709 1
< 0.1%
27.33476 1
< 0.1%
27.30005 1
< 0.1%
27.07122 1
< 0.1%
27.06348 1
< 0.1%
26.79265 1
< 0.1%

i
Real number (ℝ)

Distinct92019
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.084854
Minimum9.469903
Maximum32.14147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:31.853785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.469903
5-th percentile16.043819
Q117.732285
median19.405145
Q320.396495
95-th percentile21.607623
Maximum32.14147
Range22.671567
Interquartile range (IQR)2.66421

Descriptive statistics

Standard deviation1.7578948
Coefficient of variation (CV)0.092109417
Kurtosis-0.23484446
Mean19.084854
Median Absolute Deviation (MAD)1.218725
Skewness-0.40416674
Sum1908485.4
Variance3.0901941
MonotonicityNot monotonic
2024-03-11T01:33:31.957529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.36181 6
 
< 0.1%
24.3618 6
 
< 0.1%
18.63308 5
 
< 0.1%
20.19353 4
 
< 0.1%
20.72639 4
 
< 0.1%
20.234 4
 
< 0.1%
19.95808 4
 
< 0.1%
19.72511 4
 
< 0.1%
19.52141 4
 
< 0.1%
20.46932 4
 
< 0.1%
Other values (92009) 99955
> 99.9%
ValueCountFrequency (%)
9.469903 1
< 0.1%
10.00865 1
< 0.1%
10.05509 1
< 0.1%
10.56647 1
< 0.1%
10.87374 1
< 0.1%
10.95665 1
< 0.1%
11.26394 1
< 0.1%
11.29956 1
< 0.1%
11.31937 1
< 0.1%
11.51527 1
< 0.1%
ValueCountFrequency (%)
32.14147 1
< 0.1%
30.25009 1
< 0.1%
30.16359 1
< 0.1%
30.1546 1
< 0.1%
29.88921 1
< 0.1%
29.85405 1
< 0.1%
27.5034 1
< 0.1%
27.18834 1
< 0.1%
26.89399 1
< 0.1%
26.30939 1
< 0.1%

z
Real number (ℝ)

SKEWED 

Distinct92007
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.66881
Minimum-9999
Maximum29.38374
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size781.4 KiB
2024-03-11T01:33:32.062695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile15.778155
Q117.460677
median19.004595
Q319.92112
95-th percentile21.462771
Maximum29.38374
Range10028.384
Interquartile range (IQR)2.4604425

Descriptive statistics

Standard deviation31.728152
Coefficient of variation (CV)1.6995272
Kurtosis99381.345
Mean18.66881
Median Absolute Deviation (MAD)1.1716
Skewness-314.75942
Sum1866881
Variance1006.6756
MonotonicityNot monotonic
2024-03-11T01:33:32.168691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8269 58
 
0.1%
22.82691 25
 
< 0.1%
22.82689 25
 
< 0.1%
22.82692 12
 
< 0.1%
19.99664 4
 
< 0.1%
19.8872 4
 
< 0.1%
18.75134 4
 
< 0.1%
19.8066 4
 
< 0.1%
19.07992 4
 
< 0.1%
19.50887 4
 
< 0.1%
Other values (91997) 99856
99.9%
ValueCountFrequency (%)
-9999 1
< 0.1%
9.612333 1
< 0.1%
10.22551 1
< 0.1%
10.44131 1
< 0.1%
10.65056 1
< 0.1%
10.77889 1
< 0.1%
10.89738 1
< 0.1%
10.91847 1
< 0.1%
11.19448 1
< 0.1%
11.30247 1
< 0.1%
ValueCountFrequency (%)
29.38374 1
< 0.1%
28.79055 1
< 0.1%
28.23829 1
< 0.1%
27.80519 1
< 0.1%
27.67336 1
< 0.1%
26.59268 1
< 0.1%
26.42779 1
< 0.1%
26.2969 1
< 0.1%
26.13011 1
< 0.1%
26.0935 1
< 0.1%

run_ID
Real number (ℝ)

Distinct430
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4481.3661
Minimum109
Maximum8162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:32.270229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile1412
Q13187
median4188
Q35326
95-th percentile8038
Maximum8162
Range8053
Interquartile range (IQR)2139

Descriptive statistics

Standard deviation1964.7646
Coefficient of variation (CV)0.43842984
Kurtosis-0.60616942
Mean4481.3661
Median Absolute Deviation (MAD)1134
Skewness0.39871052
Sum4.4813661 × 108
Variance3860299.9
MonotonicityNot monotonic
2024-03-11T01:33:32.377983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3699 2450
 
2.5%
4263 2295
 
2.3%
3225 2275
 
2.3%
2964 2009
 
2.0%
7717 1782
 
1.8%
5935 1646
 
1.6%
7773 1630
 
1.6%
3926 1552
 
1.6%
7712 1422
 
1.4%
1412 1397
 
1.4%
Other values (420) 81542
81.5%
ValueCountFrequency (%)
109 46
 
< 0.1%
211 4
 
< 0.1%
273 1
 
< 0.1%
287 1
 
< 0.1%
297 1
 
< 0.1%
307 35
 
< 0.1%
308 137
 
0.1%
745 9
 
< 0.1%
752 440
0.4%
756 370
0.4%
ValueCountFrequency (%)
8162 253
0.3%
8158 10
 
< 0.1%
8157 104
 
0.1%
8156 405
0.4%
8155 8
 
< 0.1%
8150 13
 
< 0.1%
8149 11
 
< 0.1%
8116 211
0.2%
8115 98
 
0.1%
8112 45
 
< 0.1%

rerun_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
301
100000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row301
2nd row301
3rd row301
4th row301
5th row301

Common Values

ValueCountFrequency (%)
301 100000
100.0%

Length

2024-03-11T01:33:32.475083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T01:33:32.544147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
301 100000
100.0%

Most occurring characters

ValueCountFrequency (%)
3 100000
33.3%
0 100000
33.3%
1 100000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 100000
33.3%
0 100000
33.3%
1 100000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 300000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 100000
33.3%
0 100000
33.3%
1 100000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 100000
33.3%
0 100000
33.3%
1 100000
33.3%

cam_col
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.51161
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:32.604225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5869122
Coefficient of variation (CV)0.45190446
Kurtosis-1.1143049
Mean3.51161
Median Absolute Deviation (MAD)1
Skewness-0.030531673
Sum351161
Variance2.5182904
MonotonicityNot monotonic
2024-03-11T01:33:33.092753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 19573
19.6%
3 18851
18.9%
5 18537
18.5%
2 17117
17.1%
1 13227
13.2%
6 12695
12.7%
ValueCountFrequency (%)
1 13227
13.2%
2 17117
17.1%
3 18851
18.9%
4 19573
19.6%
5 18537
18.5%
6 12695
12.7%
ValueCountFrequency (%)
6 12695
12.7%
5 18537
18.5%
4 19573
19.6%
3 18851
18.9%
2 17117
17.1%
1 13227
13.2%

field_ID
Real number (ℝ)

Distinct856
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.13052
Minimum11
Maximum989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:33.185844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile31
Q182
median146
Q3241
95-th percentile489
Maximum989
Range978
Interquartile range (IQR)159

Descriptive statistics

Standard deviation149.01107
Coefficient of variation (CV)0.80057302
Kurtosis3.703783
Mean186.13052
Median Absolute Deviation (MAD)74
Skewness1.7534251
Sum18613052
Variance22204.3
MonotonicityNot monotonic
2024-03-11T01:33:33.290669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 472
 
0.5%
65 458
 
0.5%
67 449
 
0.4%
60 448
 
0.4%
69 447
 
0.4%
72 446
 
0.4%
68 445
 
0.4%
74 442
 
0.4%
81 442
 
0.4%
125 436
 
0.4%
Other values (846) 95515
95.5%
ValueCountFrequency (%)
11 157
0.2%
12 146
0.1%
13 155
0.2%
14 182
0.2%
15 162
0.2%
16 205
0.2%
17 205
0.2%
18 235
0.2%
19 236
0.2%
20 226
0.2%
ValueCountFrequency (%)
989 3
< 0.1%
982 1
 
< 0.1%
980 1
 
< 0.1%
979 1
 
< 0.1%
978 1
 
< 0.1%
977 1
 
< 0.1%
974 1
 
< 0.1%
971 1
 
< 0.1%
941 2
< 0.1%
940 3
< 0.1%

spec_obj_ID
Real number (ℝ)

UNIQUE 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7838823 × 1018
Minimum2.9951909 × 1017
Maximum1.4126941 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:33.388722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.9951909 × 1017
5-th percentile8.1865916 × 1017
Q12.8441376 × 1018
median5.6148831 × 1018
Q38.3321441 × 1018
95-th percentile1.1744357 × 1019
Maximum1.4126941 × 1019
Range1.3827422 × 1019
Interquartile range (IQR)5.4880065 × 1018

Descriptive statistics

Standard deviation3.3240162 × 1018
Coefficient of variation (CV)0.57470329
Kurtosis-0.90475989
Mean5.7838823 × 1018
Median Absolute Deviation (MAD)2.7303045 × 1018
Skewness0.19846275
Sum5.7838823 × 1023
Variance1.1049083 × 1037
MonotonicityNot monotonic
2024-03-11T01:33:33.493918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.543777369 × 10181
 
< 0.1%
5.644168765 × 10181
 
< 0.1%
9.83487601 × 10181
 
< 0.1%
3.301259275 × 10181
 
< 0.1%
4.864118044 × 10181
 
< 0.1%
3.532062986 × 10181
 
< 0.1%
1.423181233 × 10181
 
< 0.1%
5.646572847 × 10181
 
< 0.1%
5.64537163 × 10181
 
< 0.1%
5.224277647 × 10181
 
< 0.1%
Other values (99990) 99990
> 99.9%
ValueCountFrequency (%)
2.995190894 × 10171
< 0.1%
2.995792876 × 10171
< 0.1%
2.995801123 × 10171
< 0.1%
2.995908325 × 10171
< 0.1%
2.995935813 × 10171
< 0.1%
2.99612273 × 10171
< 0.1%
2.99621344 × 10171
< 0.1%
2.996243676 × 10171
< 0.1%
2.99643884 × 10171
< 0.1%
2.996455332 × 10171
< 0.1%
ValueCountFrequency (%)
1.412694061 × 10191
< 0.1%
1.412693374 × 10191
< 0.1%
1.412693346 × 10191
< 0.1%
1.4126851 × 10191
< 0.1%
1.41268411 × 10191
< 0.1%
1.412683808 × 10191
< 0.1%
1.412680509 × 10191
< 0.1%
1.412674874 × 10191
< 0.1%
1.412674655 × 10191
< 0.1%
1.41267405 × 10191
< 0.1%

class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
GALAXY
59445 
STAR
21594 
QSO
18961 

Length

Max length6
Median length6
Mean length4.99929
Min length3

Characters and Unicode

Total characters499929
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGALAXY
2nd rowGALAXY
3rd rowGALAXY
4th rowGALAXY
5th rowGALAXY

Common Values

ValueCountFrequency (%)
GALAXY 59445
59.4%
STAR 21594
 
21.6%
QSO 18961
 
19.0%

Length

2024-03-11T01:33:33.597381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T01:33:33.674026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
galaxy 59445
59.4%
star 21594
 
21.6%
qso 18961
 
19.0%

Most occurring characters

ValueCountFrequency (%)
A 140484
28.1%
G 59445
11.9%
L 59445
11.9%
X 59445
11.9%
Y 59445
11.9%
S 40555
 
8.1%
T 21594
 
4.3%
R 21594
 
4.3%
Q 18961
 
3.8%
O 18961
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 499929
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 140484
28.1%
G 59445
11.9%
L 59445
11.9%
X 59445
11.9%
Y 59445
11.9%
S 40555
 
8.1%
T 21594
 
4.3%
R 21594
 
4.3%
Q 18961
 
3.8%
O 18961
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 499929
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 140484
28.1%
G 59445
11.9%
L 59445
11.9%
X 59445
11.9%
Y 59445
11.9%
S 40555
 
8.1%
T 21594
 
4.3%
R 21594
 
4.3%
Q 18961
 
3.8%
O 18961
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 499929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 140484
28.1%
G 59445
11.9%
L 59445
11.9%
X 59445
11.9%
Y 59445
11.9%
S 40555
 
8.1%
T 21594
 
4.3%
R 21594
 
4.3%
Q 18961
 
3.8%
O 18961
 
3.8%

redshift
Real number (ℝ)

Distinct99295
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5766608
Minimum-0.009970667
Maximum7.011245
Zeros412
Zeros (%)0.4%
Negative13724
Negative (%)13.7%
Memory size781.4 KiB
2024-03-11T01:33:33.760143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-0.009970667
5-th percentile-0.00032605719
Q10.054516845
median0.42417325
Q30.70415432
95-th percentile2.1877278
Maximum7.011245
Range7.0212157
Interquartile range (IQR)0.64963748

Descriptive statistics

Standard deviation0.73070728
Coefficient of variation (CV)1.2671353
Kurtosis9.9729155
Mean0.5766608
Median Absolute Deviation (MAD)0.34491005
Skewness2.5236063
Sum57666.08
Variance0.53393312
MonotonicityNot monotonic
2024-03-11T01:33:33.867718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 412
 
0.4%
7.011245 6
 
< 0.1%
0.004153254 4
 
< 0.1%
0.004153253 3
 
< 0.1%
-0.004136076 3
 
< 0.1%
0.6291372 3
 
< 0.1%
0.471631 2
 
< 0.1%
0.8155696 2
 
< 0.1%
1.054235 2
 
< 0.1%
0.7730237 2
 
< 0.1%
Other values (99285) 99561
99.6%
ValueCountFrequency (%)
-0.009970667 1
 
< 0.1%
-0.007351653 1
 
< 0.1%
-0.006863183 1
 
< 0.1%
-0.006055369 1
 
< 0.1%
-0.005675106 1
 
< 0.1%
-0.004712832 1
 
< 0.1%
-0.004254519 1
 
< 0.1%
-0.004136078 1
 
< 0.1%
-0.004136076 3
< 0.1%
-0.004020985 1
 
< 0.1%
ValueCountFrequency (%)
7.011245 6
< 0.1%
7.011103 1
 
< 0.1%
7.010295 1
 
< 0.1%
7.010272 1
 
< 0.1%
7.010263 1
 
< 0.1%
7.009989 1
 
< 0.1%
7.0094 1
 
< 0.1%
7.008322 1
 
< 0.1%
7.007396 1
 
< 0.1%
7.00387 2
 
< 0.1%

plate
Real number (ℝ)

Distinct6284
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5137.0097
Minimum266
Maximum12547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:33.975907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum266
5-th percentile727
Q12526
median4987
Q37400.25
95-th percentile10431
Maximum12547
Range12281
Interquartile range (IQR)4874.25

Descriptive statistics

Standard deviation2952.3034
Coefficient of variation (CV)0.57471244
Kurtosis-0.90475111
Mean5137.0097
Median Absolute Deviation (MAD)2425
Skewness0.1984718
Sum5.1370097 × 108
Variance8716095.1
MonotonicityNot monotonic
2024-03-11T01:33:34.083027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6301 98
 
0.1%
7699 97
 
0.1%
7407 96
 
0.1%
7147 95
 
0.1%
6516 94
 
0.1%
5185 88
 
0.1%
7697 83
 
0.1%
7450 80
 
0.1%
10431 79
 
0.1%
7701 78
 
0.1%
Other values (6274) 99112
99.1%
ValueCountFrequency (%)
266 14
< 0.1%
267 12
< 0.1%
268 1
 
< 0.1%
269 2
 
< 0.1%
271 3
 
< 0.1%
272 2
 
< 0.1%
273 1
 
< 0.1%
274 2
 
< 0.1%
276 1
 
< 0.1%
277 2
 
< 0.1%
ValueCountFrequency (%)
12547 13
< 0.1%
12545 14
< 0.1%
12544 26
< 0.1%
12533 8
 
< 0.1%
12531 30
< 0.1%
12527 17
< 0.1%
12525 31
< 0.1%
11704 2
 
< 0.1%
11678 5
 
< 0.1%
11677 6
 
< 0.1%

MJD
Real number (ℝ)

Distinct2180
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55588.647
Minimum51608
Maximum58932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:34.184397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum51608
5-th percentile52337.45
Q154234
median55868.5
Q356777
95-th percentile58200
Maximum58932
Range7324
Interquartile range (IQR)2543

Descriptive statistics

Standard deviation1808.4842
Coefficient of variation (CV)0.032533338
Kurtosis-0.77315233
Mean55588.647
Median Absolute Deviation (MAD)1256.5
Skewness-0.38185318
Sum5.5588648 × 109
Variance3270615.2
MonotonicityNot monotonic
2024-03-11T01:33:34.292487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56625 249
 
0.2%
58162 228
 
0.2%
56658 217
 
0.2%
56390 216
 
0.2%
56385 201
 
0.2%
57574 200
 
0.2%
58428 193
 
0.2%
56074 190
 
0.2%
56722 188
 
0.2%
55863 186
 
0.2%
Other values (2170) 97932
97.9%
ValueCountFrequency (%)
51608 12
< 0.1%
51609 7
< 0.1%
51613 4
 
< 0.1%
51615 4
 
< 0.1%
51630 14
< 0.1%
51633 1
 
< 0.1%
51637 6
< 0.1%
51658 2
 
< 0.1%
51662 7
< 0.1%
51663 11
< 0.1%
ValueCountFrequency (%)
58932 61
0.1%
58931 8
 
< 0.1%
58930 31
< 0.1%
58928 39
< 0.1%
58895 2
 
< 0.1%
58543 36
< 0.1%
58526 36
< 0.1%
58523 36
< 0.1%
58522 40
< 0.1%
58515 39
< 0.1%

fiber_ID
Real number (ℝ)

Distinct1000
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.31274
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-03-11T01:33:34.404587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1221
median433
Q3645
95-th percentile926
Maximum1000
Range999
Interquartile range (IQR)424

Descriptive statistics

Standard deviation272.4984
Coefficient of variation (CV)0.60647825
Kurtosis-0.98072189
Mean449.31274
Median Absolute Deviation (MAD)212
Skewness0.22971912
Sum44931274
Variance74255.38
MonotonicityNot monotonic
2024-03-11T01:33:34.511214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
637 159
 
0.2%
105 158
 
0.2%
597 158
 
0.2%
611 154
 
0.2%
321 154
 
0.2%
621 151
 
0.2%
564 149
 
0.1%
409 148
 
0.1%
563 148
 
0.1%
571 146
 
0.1%
Other values (990) 98475
98.5%
ValueCountFrequency (%)
1 86
0.1%
2 114
0.1%
3 135
0.1%
4 87
0.1%
5 117
0.1%
6 108
0.1%
7 117
0.1%
8 101
0.1%
9 108
0.1%
10 100
0.1%
ValueCountFrequency (%)
1000 81
0.1%
999 88
0.1%
998 72
0.1%
997 100
0.1%
996 70
0.1%
995 63
0.1%
994 57
0.1%
993 78
0.1%
992 64
0.1%
991 59
0.1%

Interactions

2024-03-11T01:33:28.774149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:09.324014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:10.633991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:11.883404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:13.300984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:14.498573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:15.705312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:16.985481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:18.431670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:19.639304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:20.927190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:22.139309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:23.358310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:24.659713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:26.265693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:27.504773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:28.854195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:09.411137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:10.716592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:11.962942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:13.381076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:14.579618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:15.790105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:17.063432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:18.511966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:19.726064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:21.007509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:22.219479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-11T01:33:14.348644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:15.552529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:16.824787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:18.283672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:19.488303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:20.766560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:21.988068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:23.206970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:24.499424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:26.107963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:27.349838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:28.617751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:29.929963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:10.556847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:11.808044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:13.229396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:14.426713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:15.629863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:16.908419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:18.361252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:19.567575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:20.850172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:22.066803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:23.286014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:24.581849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:26.189106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:27.431269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T01:33:28.698084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-11T01:33:30.036032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-11T01:33:30.275036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
01.237661e+18135.68910732.49463223.8788222.2753020.3950119.1657318.7937136063012796.543777e+18GALAXY0.634794581256354171
11.237665e+18144.82610131.27418524.7775922.8318822.5844421.1681221.61427451830151191.176014e+19GALAXY0.7791361044558158427
21.237661e+18142.18879035.58244425.2630722.6638920.6097619.3485718.94827360630121205.152200e+18GALAXY0.644195457655592299
31.237663e+18338.741038-0.40282822.1368223.7765621.6116220.5045419.25010419230132141.030107e+19GALAXY0.932346914958039775
41.237680e+18345.28259321.18386619.4371817.5802816.4974715.9771115.54461810230131376.891865e+18GALAXY0.116123612156187842
51.237680e+18340.99512120.58947623.4882723.3377621.3219520.2561519.54544810230131105.658977e+18QSO1.424659502655855741
61.237679e+1823.23492611.41818821.4697321.1762420.9282920.6082620.42573777330124621.246262e+19QSO0.5864551106958456113
71.237679e+185.43317612.06518622.2497922.0217220.3412619.4879418.84999777330123466.961443e+18GALAXY0.47700961835621015
81.237661e+18200.29047547.19940224.4028622.3566920.6103219.4649018.95852371630151087.459285e+18GALAXY0.660012662556386719
91.237671e+1839.14969128.10284221.7466920.0349319.1755318.8182318.65422593430141222.751763e+18STAR-0.000008244454082232
obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
999901.237665e+18143.41477426.73110823.7965522.4133220.3601219.3396418.74413456930112136.525784e+18GALAXY0.555789579656274247
999911.237665e+18146.89805527.81809426.2289921.8482020.4713819.6004918.94726456930112346.523611e+18GALAXY0.550025579456282535
999921.237668e+18129.86276214.57431225.8223424.0272221.4313720.1308519.66464513730131115.066775e+18GALAXY0.587944450055543819
999931.237653e+180.740551-9.18424323.1317223.1850621.5285420.2697520.1138317403015278.069569e+18GALAXY0.617036716756604889
999941.237663e+18317.246996-0.68225420.9652619.8162519.3418619.1471119.0579041873012641.154061e+18GALAXY0.17520610255323951
999951.237679e+1839.620709-2.59407422.1675922.9758621.9040421.3054820.73569777830125811.055431e+19GALAXY0.000000937457749438
999961.237679e+1829.49381919.79887422.6911822.3862820.4500319.7575919.41526791730112898.586351e+18GALAXY0.404895762656934866
999971.237668e+18224.58740715.70070721.1691619.2699718.2042817.6903417.35221531430143083.112008e+18GALAXY0.14336627645453574
999981.237661e+18212.26862146.66036525.3503921.6375719.9138619.0725418.62482365030141317.601080e+18GALAXY0.455040675156368470
999991.237661e+18196.89605349.46464322.6217121.7974520.6011520.0095919.2807536503014608.343152e+18GALAXY0.542944741057104851